ArticlePLoS computational biology2022
Secuer: Ultrafast, scalable and accurate clustering of single-cell RNA-seq data.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Navigating the Landscape of Cytometry-Based Single-Cell Proteomics: Quantification, Annotation, and Resources.International journal of molecular sciences · 2026Review
- Consensus spectral clustering with weighted similarity functions for single-cell RNA sequencing data.Communications in statistics: Simulation and computation · 2025Article
- scAGCI: an anchor graph-based method for cell clustering from integrated scRNA-seq and scATAC-seq data.Briefings in bioinformatics · 2025Article
- Navigating single-cell RNA-sequencing: protocols, tools, databases, and applications.Genomics & informatics · 2025Review
- A scalable approach to topic modelling in single-cell data by approximate pseudobulk projection.Life science alliance · 2024Article
- scDFN: enhancing single-cell RNA-seq clustering with deep fusion networks.Briefings in bioinformatics · 2024Article
- CTEC: a cross-tabulation ensemble clustering approach for single-cell RNA sequencing data analysis.Bioinformatics (Oxford, England) · 2024Article
- Computational single cell oncology: state of the art.Frontiers in genetics · 2023Review
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5 authors.
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Abstract
Identifying cell clusters is a critical step for single-cell transcriptomics study. Despite the numerous clustering tools developed recently, the rapid growth of scRNA-seq volumes prompts for a more (computationally) efficient clustering method. Here, we introduce Secuer, a Scalable and Efficient speCtral clUstERing algorithm for scRNA-seq data. By employing an anchor-based bipartite graph representation algorithm, Secuer enjoys reduced runtime and memory usage over one order of magnitude for datasets with more than 1 million cells. Meanwhile, Secuer also achieves better or comparable accuracy than competing methods in small and moderate benchmark datasets. Furthermore, we showcase that Secuer can also serve as a building block for a new consensus clustering method, Secuer-consensus, which again improves the runtime and scalability of state-of-the-art consensus clustering methods while also maintaining the accuracy. Overall, Secuer is a versatile, accurate, and scalable clustering framework suitable for small to ultra-large single-cell clustering tasks.
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